TECHNICAL FIELD

The present disclosure relates to wireless communications, and in particular, to estimating an expected downlink channel quality, such as an expected signal to interference plus noise ratio (SINR), and associated uncertainty for use in link adaptation.

BACKGROUND

The Third Generation Partnership Project (3GPP) has developed and is developing standards for Fourth Generation (4G) (also referred to as Long Term Evolution (LTE)) and Fifth Generation (5G) (also referred to as New Radio (NR)) wireless communication systems. Such systems provide, among other features, broadband communication between network nodes, such as base stations, and mobile wireless devices (WD), as well as communication between network nodes and between WDs. Sixth Generation (6G) wireless communication systems are also under development.

The scheduler in a NR base station (gNB), hereafter referred to as a network node, is responsible for resource allocation for WDs in a connected mode in both uplink and downlink communications. The scheduler receives from the core the input related to the required quality of service (QoS) for each WD and service. The scheduler works closely with a link adaptation (LA) process to select the proper transport block formats for uplink and downlink transmissions. The LA process determines a radio resource assignment for a WD based on estimated signal to interference plus noise ratio (SINR), the outcome of WD’s previous transmission, as communicated from the WD to the network node in an acknowledgement/non- acknowledgement (ACK/NACK), a WD’s power headroom, and the available bandwidth.

FIG. 1 illustrates an example of interaction between a scheduler 2, a QoS unit 4 in a core network node, a link adapter 6 and a power control unit 8 in the network node (gNB). The scheduler 2 and link adapter 6 have the task of selecting a transport format that includes a resource allocation, a number of layers, and a modulation and coding scheme (MCS) for a downlink transmission based on channel state information (CSI) from the WD. For uplink transmissions, the selection is based on CSI measured by the network node. For services with high reliability requirements, this can be a challenging task, especially if the transport format also should be spectrally efficient. Since the transmission for which link adaption is performed occurs later than when the CSI was measured, the scheduler 2 and link adapter 6 predict the SINR at the time of transmission based on an observed SINR that can be determined from reported or measured CSI. One prediction that is typically used is the last reported and measured SINR.

Uncertainty in link adaption

A fundamental problem with link adaption is that in general it is impossible for the network node to determine a predicted SINR that is always the same as the SINR that the transmission will experience. Hence, to any predicted SINR there is an associated uncertainty. Traditional link adaptation mitigates this uncertainty by relying on hybrid automatic request (HARQ) re-transmissions. However, for services with latency requirements, the number of HARQ re-transmissions that can be performed may be rather few, which means that the link adaptation process should account for the uncertainty in the predicted SINR based on measured SINR.

The uncertainty in the predicted SINR may depend on several factors which may include:

• Age of CSI report;

• CSI report quantization;

• Channel quality indicator (CQI) to channel quality mapping error;

• Channel variations, e.g., fading and mobility;

• Intercell/Intracell Interference variation; and/or

• Measurement error such as sounding reference signal (SRS) or channel state information reference signal interference measurement (CSI- RS/IM) measurement errors.

A known approach to account for the uncertainty in the predicted SINR is to select a backoff to the predicted SINR (subtraction in the logarithm domain): SIN R _{used-prediction} SI N R _predicted backoof f , where SINR _{used-prediction} is the SINR used as a prediction and SINR _predicted is the predicted SINR that often is the same as the last “observed” SINR. In some cases, the SINR _predicted may be determined accounting for one or more of the factors that contributes to the uncertainty. In such cases, the remaining uncertainty may be reduced and hence, backoff can also be reduced.

In a typical link adaptation, an MCS value is selected to achieve an SINR equal to SINR _{used-prediction} such that the BLEP (Block-Error Probability) does not exceed

10%.

Link adaptation for reciprocity-based beamforming and multi-user multiple- input-multiple-output (MU-MIMO) scheduling

In time division duplex (TDD) systems, downlink (DL) and uplink (UL) channels may be assumed to be reciprocal. The DL channel H can be estimated in the network node from SRS (Sounding Reference Signals) measurements. With knowledge of the channel matrix H, the network node may choose from an infinite set of precoders, instead of using the pre-coder suggested by the WD, (where the suggested precoder is deduced by the WD from a finite set of pre-coders in a pre-coder codebook.)

Still, the network node cannot directly determine SINR from H since the network node does not know the noise and interference covariance matrix Q. Hence, CSI reports are still needed by the network node to produce an estimate of Q. The covariance matrix Q may be estimated in different ways.

If effective channel H _P = HP, where H is the channel and P is the pre-coder, and Q are known, then SINR(l) for layer I is determined according to the following formula if the receiver is a minimum mean squared error interference rejection combining (MMSE-IRC) receiver: (1) where * denotes Hermitian conjugate, and A(l, Z) denotes the Z-th diagonal element.

From (1) it is possible to determine Q in at least one of at least two ways:

1. Solve (1) for Q with diagonal H _signal , where diagonal elements correspond to SINR _CSI deduced from CSI report; or 2. Find Q = ql, where q is scalar and I is the identity matrix, such that (1) yields SINR(T) = SINR _CSI .

When determining an estimate of the covariance Q, P is the pre-coder reported by the WD while H is the channel network node determined from the sounding reference signal (SRS). However, since a pre-coder that will be used for the transmission is not the pre-coder reported by the WD, the network node calculates SINR for the pre-coder that will be used.

For multi-user multiple-input-multiple-output (MU-MIMO) configurations, the covariance Q further includes contributions from co-scheduled WDs. More precisely, if n WDs are co-scheduled using pre-coders P ₀ > P ₁ > — > P _n-1 together with the current WD, then the total covariance Q _tot experienced by the current WD equals Q _tot = Q + where the network node should account for the available power that is divided among the co-scheduled WDs.

To determine a suitable backoff is difficult and heavily impacts system performance. If the selected backoff is too small, then the reliability requirement may not be fulfilled and if the selected backoff is too large, then spectral efficiency is reduced. Low spectral efficiency may lead to degraded performance for other services such as evolved Mobile Broadband (eMBB), and system capacity is impacted by reducing the number of users for which the reliability requirement can be met.

A suitable backoff may be difficult to determine and SINR _predicted may be difficult to estimate. For example, if SINR _predicted is determined based on the statistics of observed SINR values (such as mean and variance), then SINR _predicted may become less accurate with smaller numbers of observed SINR values or SINR _predicted may become less accurate if the statistics are based on observed SINR values that are not representative of current radio conditions (e.g., the WD has moved, or interference patterns change). It is also difficult to determine an SINR prediction that leads to a small remaining uncertainty. For example, CSI reported by the WD includes rank, precoder and CQI values where a CQI value is conditioned on a reported rank and precoder. From a CQI value, the network node can deduce a corresponding SINR value but will not be able to determine what SINR value would hold for some other rank and pre-coder.

Another problem is how to select a target BLEP in LA as it is known that a target BLEP of 10% is generally not optimal.

SUMMARY

Some embodiments advantageously provide methods, systems, and apparatuses for estimating an expected downlink signal to interference ratio and associated uncertainty for use in link adaptation.

In some embodiments, methods for estimating expected channel quality, such as an expected SINR, and determining an associated uncertainty to be used in link adaption is provided. In some embodiments, a method may include one or more of the following: transforming reported channel quality for a given rank to a domain independent of rank; determining of statistical measures such as mean, variance or standard deviation (std), and time auto-correlation in the rank-independent domain; and/or determining rank-specific estimates of expected channel quality (such as expected SINR) and associated uncertainty based on a latest received CSI report and determined statistical measures.

In some embodiments, a method may include one or more of the following: transforming reported channel quality for a given rank to a domain independent of rank; determination of statistical measures such as mean, variance/std, and time auto-correlation in the rank-independent domain; and/or determining rank-specific estimates of expected channel quality and associated uncertainty based on latest received CSI report and determined statistical measures.

Some embodiments provide improved spectral efficiency. According to one aspect, a method in a network node configured to communicate with a wireless device, WD, is provided. The method includes mapping each of a plurality of channel quality values according to a first mapping function, the first mapping function being based at least in part on a base rank and a second rank. The method also includes determining a base mean and a base variance of the mapped channel quality values. The method further includes, given a first mapped channel quality value of the mapped channel quality values, determining a second mean and a second variance based at least in part on the base mean, the base variance, the first mapped channel quality value and an autocorrelation of the mapped channel quality values. The method also includes mapping the second mean according to a second mapping function, the second mapping function being based at least in part on the base rank and a selected rank, to determine an estimated channel quality value expected at a future time for the selected rank.

According to this aspect, in some embodiments, the method includes comparing the second variance to a first threshold, the first threshold being associated with a maximum block error probability, BLEP. In some embodiments, the method also includes comparing the second variance to a second threshold, the second threshold being associated with a minimum block error probability, BLEP. In some embodiments, the second mapping function is based at least in part on an inverse of the first mapping function. In some embodiments, the first mapping function includes: where Q is a measured signal channel quality value, RIbase is a base rank indicator and RI is a reported rank indicator, k _RI is a constant selected based at least in part on whether the base rank is greater than the second rank. In some embodiments, the method further includes measuring the plurality of channel quality values at successive times based at least in part on a rank reported by the WD and a channel quality index, CQI, reported by the WD. In some embodiments, the plurality of measured channel quality values are filtered prior to determining the base mean and the base variance. In some embodiments, the plurality of measured channel quality values are weighted to give greater weight to more recent measured channel quality values than weight given to less recent measured channel quality values. In some embodiments, determining the second mean and the second variance further includes interpolating the autocorrelation of the mapped channel quality values. In some embodiments, the method also includes determining the plurality of channel quality values based at least in part on a random variable. In some embodiments, determining the base mean and the base variance is based at least in part on determining a mean and variance for each of a plurality of sub-bands. In some embodiments, the second rank is one of a rank intended for downlink transmission and a rank last reported by the WD, and wherein the selected rank is the rank intended for downlink transmission. In some embodiments, a channel quality value is a signal to interference plus noise ratio, SINR.

According to another aspect, a network node configured to communicate with a wireless device (WD) is provided. The network node includes processing circuitry configured to: map each of a plurality of channel quality values according to a first mapping function, the first mapping function being based at least in part on a base rank and a second rank; determine a base mean and a base variance of the mapped channel quality values; given a first mapped channel quality value of the mapped channel quality values, determine a second mean and a second variance based at least in part on the base mean, the base variance, the first mapped channel quality value and an autocorrelation of the mapped channel quality values; and map the second mean according to a second mapping function, the second mapping function being based at least in part on the base rank and a selected rank, to determine an estimated channel quality value expected at a future time for the selected rank.

According to this aspect, in some embodiments, the processing circuitry is further configured to compare the second variance to a first threshold, the first threshold being associated with a maximum block error probability, BLEP. In some embodiments, the processing circuitry is further configured to compare the second variance to a second threshold, the second threshold being associated with a minimum block error probability, BLEP. In some embodiments, the second mapping function is based at least in part on an inverse of the first mapping function. In some embodiments, the first mapping function includes: where Q is a measured channel quality value, RIbase is a base rank indicator and RI is a reported rank indicator, kiu is a constant selected based at least in part on whether the base rank is greater than the second rank, and where SINR is a measured SINR. In some embodiments, the processing circuitry is further configured to measure the plurality of channel quality values at successive times based at least in part on a rank reported by the WD and a channel quality index, CQI, reported by the WD. In some embodiments, the plurality of measured channel quality values are filtered prior to determining the base mean and the base variance. In some embodiments, the plurality of measured channel quality values are weighted to give greater weight to more recent measured channel quality values than weight given to less recent measured channel quality values. In some embodiments, determining the second mean and the second variance further includes interpolating the autocorrelation of the mapped channel quality values. In some embodiments, the processing circuitry is further configured to determine the plurality of channel quality values based at least in part on a random variable. In some embodiments, determining the base mean and the base variance is based at least in part on determining a mean and variance for each a plurality of subbands. In some embodiments, the second rank is one of a rank intended for downlink transmission and a rank last reported by the WD, and wherein the selected rank is the rank intended for downlink transmission. In some embodiments, a channel quality value is a signal to interference plus noise ratio, SINR.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the present embodiments, and the attendant advantages and features thereof, will be more readily understood by reference to the following detailed description when considered in conjunction with the accompanying drawings wherein:

FIG. l is a flow diagram between components involved in a scheduling and link adaptation process;

FIG. 2 is a schematic diagram of an exemplary network architecture illustrating a communication system connected via an intermediate network to a host computer according to the principles in the present disclosure; FIG. 3 is a block diagram of a host computer communicating via a network node with a wireless device over an at least partially wireless connection according to some embodiments of the present disclosure;

FIG. 4 is a flowchart illustrating exemplary methods implemented in a communication system including a host computer, a network node and a wireless device for executing a client application at a wireless device according to some embodiments of the present disclosure;

FIG. 5 is a flowchart illustrating exemplary methods implemented in a communication system including a host computer, a network node and a wireless device for receiving user data at a wireless device according to some embodiments of the present disclosure;

FIG. 6 is a flowchart illustrating exemplary methods implemented in a communication system including a host computer, a network node and a wireless device for receiving user data from the wireless device at a host computer according to some embodiments of the present disclosure;

FIG. 7 is a flowchart illustrating exemplary methods implemented in a communication system including a host computer, a network node and a wireless device for receiving user data at a host computer according to some embodiments of the present disclosure; and

FIG. 8 is a flowchart of an exemplary process in a network node for estimating an expected downlink signal to interference ratio and associated uncertainty for use in link adaptation.

DETAILED DESCRIPTION

Before describing in detail exemplary embodiments, it is noted that the embodiments reside primarily in combinations of apparatus components and processing steps related to estimating an expected downlink signal to interference ratio and associated uncertainty for use in link adaptation. Accordingly, components have been represented where appropriate by conventional symbols in the drawings, showing only those specific details that are pertinent to understanding the embodiments so as not to obscure the disclosure with details that will be readily apparent to those of ordinary skill in the art having the benefit of the description herein. Like numbers refer to like elements throughout the description.

As used herein, relational terms, such as “first” and “second,” “top” and “bottom,” and the like, may be used solely to distinguish one entity or element from another entity or element without necessarily requiring or implying any physical or logical relationship or order between such entities or elements. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the concepts described herein. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including” when used herein, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

In embodiments described herein, the joining term, “in communication with” and the like, may be used to indicate electrical or data communication, which may be accomplished by physical contact, induction, electromagnetic radiation, radio signaling, infrared signaling or optical signaling, for example. One having ordinary skill in the art will appreciate that multiple components may interoperate and modifications and variations are possible of achieving the electrical and data communication.

In some embodiments described herein, the term “coupled,” “connected,” and the like, may be used herein to indicate a connection, although not necessarily directly, and may include wired and/or wireless connections.

The term “network node” used herein can be any kind of network node comprised in a radio network which may further comprise any of base station (BS), radio base station, base transceiver station (BTS), base station controller (BSC), radio network controller (RNC), g Node B (gNB), evolved Node B (eNB or eNodeB), Node B, multi-standard radio (MSR) radio node such as MSR BS, multi-cell/multicast coordination entity (MCE), integrated access and backhaul (IAB) node, relay node, donor node controlling relay, radio access point (AP), transmission points, transmission nodes, Remote Radio Unit (RRU) Remote Radio Head (RRH), a core network node (e.g., mobile management entity (MME), self-organizing network (SON) node, a coordinating node, positioning node, MDT node, etc.), an external node (e.g., 3rd party node, a node external to the current network), nodes in distributed antenna system (DAS), a spectrum access system (SAS) node, an element management system (EMS), etc. The network node may also comprise test equipment. The term “radio node” used herein may be used to also denote a wireless device (WD) such as a wireless device (WD) or a radio network node.

In some embodiments, the non-limiting terms wireless device (WD) or a user equipment (UE) are used interchangeably. The WD herein can be any type of wireless device capable of communicating with a network node or another WD over radio signals, such as wireless device (WD). The WD may also be a radio communication device, target device, device to device (D2D) WD, machine type WD or WD capable of machine to machine communication (M2M), low-cost and/or low-complexity WD, a sensor equipped with WD, Tablet, mobile terminals, smart phone, laptop embedded equipped (LEE), laptop mounted equipment (LME), USB dongles, Customer Premises Equipment (CPE), an Internet of Things (loT) device, or a Narrowband loT (NB-IOT) device, etc.

Also, in some embodiments the generic term “radio network node” is used. It can be any kind of a radio network node which may comprise any of base station, radio base station, base transceiver station, base station controller, network controller, RNC, evolved Node B (eNB), Node B, gNB, Multi-cell/multicast Coordination Entity (MCE), IAB node, relay node, access point, radio access point, Remote Radio Unit (RRU) Remote Radio Head (RRH).

Note that although terminology from one particular wireless system, such as, for example, 3GPP LTE and/or New Radio (NR), may be used in this disclosure, this should not be seen as limiting the scope of the disclosure to only the aforementioned system. Other wireless systems, including without limitation Wide Band Code Division Multiple Access (WCDMA), Worldwide Interoperability for Microwave Access (WiMax), Ultra Mobile Broadband (UMB) and Global System for Mobile Communications (GSM), may also benefit from exploiting the ideas covered within this disclosure. Note further, that functions described herein as being performed by a wireless device or a network node may be distributed over a plurality of wireless devices and/or network nodes. In other words, it is contemplated that the functions of the network node and wireless device described herein are not limited to performance by a single physical device and, in fact, can be distributed among several physical devices.

Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. It will be further understood that terms used herein should be interpreted as having a meaning that is consistent with their meaning in the context of this specification and the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

Some embodiments provide estimating an expected downlink signal to interference ratio and associated uncertainty for use in link adaptation.

Returning now to the drawing figures, in which like elements are referred to by like reference numerals, there is shown in FIG. 2 a schematic diagram of a communication system 10, according to an embodiment, such as a 3 GPP -type cellular network that may support standards such as LTE and/or NR (5G), which comprises an access network 12, such as a radio access network, and a core network 14. The access network 12 comprises a plurality of network nodes 16a, 16b, 16c (referred to collectively as network nodes 16), such as NBs, eNBs, gNBs or other types of wireless access points, each defining a corresponding coverage area 18a, 18b, 18c (referred to collectively as coverage areas 18). Each network node 16a, 16b, 16c is connectable to the core network 14 over a wired or wireless connection 20. A first wireless device (WD) 22a located in coverage area 18a is configured to wirelessly connect to, or be paged by, the corresponding network node 16a. A second WD 22b in coverage area 18b is wirelessly connectable to the corresponding network node 16b. While a plurality of WDs 22a, 22b (collectively referred to as wireless devices 22) are illustrated in this example, the disclosed embodiments are equally applicable to a situation where a sole WD is in the coverage area or where a sole WD is connecting to the corresponding network node 16. Note that although only two WDs 22 and three network nodes 16 are shown for convenience, the communication system may include many more WDs 22 and network nodes 16.

Also, it is contemplated that a WD 22 can be in simultaneous communication and/or configured to separately communicate with more than one network node 16 and more than one type of network node 16. For example, a WD 22 can have dual connectivity with a network node 16 that supports LTE and the same or a different network node 16 that supports NR. As an example, WD 22 can be in communication with an eNB for LTE/E-UTRAN and a gNB for NR/NG-RAN.

The communication system 10 may itself be connected to a host computer 24, which may be embodied in the hardware and/or software of a standalone server, a cloud-implemented server, a distributed server or as processing resources in a server farm. The host computer 24 may be under the ownership or control of a service provider, or may be operated by the service provider or on behalf of the service provider. The connections 26, 28 between the communication system 10 and the host computer 24 may extend directly from the core network 14 to the host computer 24 or may extend via an optional intermediate network 30. The intermediate network 30 may be one of, or a combination of more than one of, a public, private or hosted network. The intermediate network 30, if any, may be a backbone network or the Internet. In some embodiments, the intermediate network 30 may comprise two or more sub-networks (not shown).

The communication system of FIG. 2 as a whole enables connectivity between one of the connected WDs 22a, 22b and the host computer 24. The connectivity may be described as an over-the-top (OTT) connection. The host computer 24 and the connected WDs 22a, 22b are configured to communicate data and/or signaling via the OTT connection, using the access network 12, the core network 14, any intermediate network 30 and possible further infrastructure (not shown) as intermediaries. The OTT connection may be transparent in the sense that at least some of the participating communication devices through which the OTT connection passes are unaware of routing of uplink and downlink communications. For example, a network node 16 may not or need not be informed about the past routing of an incoming downlink communication with data originating from a host computer 24 to be forwarded (e.g., handed over) to a connected WD 22a. Similarly, the network node 16 need not be aware of the future routing of an outgoing uplink communication originating from the WD 22a towards the host computer 24.

A network node 16 is configured to include a mapping unit 32 which may be configured to map each of a plurality of channel quality values according to a first mapping function, the first mapping function being based at least in part on a base rank and a second rank. The network node may also include a statistics unit 34 which is configured to determine a base mean and a base variance of the mapped channel quality values. The mapping unit 32 may also be configured to map a second mean according to a second mapping function, the second mapping function being based on the base rank and a selected rank, to determine an estimated channel quality value expected at a future time for the selected rank. The statistics unit 34 may also be configured to, given a first mapped channel quality value of the mapped channel quality values, determine a second mean and a second variance based at least in part on the base mean, the base variance, the first mapped channel quality value and an autocorrelation of the mapped channel quality values.

Example implementations, in accordance with an embodiment, of the WD 22, network node 16 and host computer 24 discussed in the preceding paragraphs will now be described with reference to FIG. 3. In a communication system 10, a host computer 24 comprises hardware (HW) 38 including a communication interface 40 configured to set up and maintain a wired or wireless connection with an interface of a different communication device of the communication system 10. The host computer 24 further comprises processing circuitry 42, which may have storage and/or processing capabilities. The processing circuitry 42 may include a processor 44 and memory 46. In particular, in addition to or instead of a processor, such as a central processing unit, and memory, the processing circuitry 42 may comprise integrated circuitry for processing and/or control, e.g., one or more processors and/or processor cores and/or FPGAs (Field Programmable Gate Array) and/or ASICs (Application Specific Integrated Circuitry) adapted to execute instructions. The processor 44 may be configured to access (e.g., write to and/or read from) memory 46, which may comprise any kind of volatile and/or nonvolatile memory, e.g., cache and/or buffer memory and/or RAM (Random Access Memory) and/or ROM (Read- Only Memory) and/or optical memory and/or EPROM (Erasable Programmable Read-Only Memory).

Processing circuitry 42 may be configured to control any of the methods and/or processes described herein and/or to cause such methods, and/or processes to be performed, e.g., by host computer 24. Processor 44 corresponds to one or more processors 44 for performing host computer 24 functions described herein. The host computer 24 includes memory 46 that is configured to store data, programmatic software code and/or other information described herein. In some embodiments, the software 48 and/or the host application 50 may include instructions that, when executed by the processor 44 and/or processing circuitry 42, causes the processor 44 and/or processing circuitry 42 to perform the processes described herein with respect to host computer 24. The instructions may be software associated with the host computer 24.

The software 48 may be executable by the processing circuitry 42. The software 48 includes a host application 50. The host application 50 may be operable to provide a service to a remote user, such as a WD 22 connecting via an OTT connection 52 terminating at the WD 22 and the host computer 24. In providing the service to the remote user, the host application 50 may provide user data which is transmitted using the OTT connection 52. The “user data” may be data and information described herein as implementing the described functionality. In one embodiment, the host computer 24 may be configured for providing control and functionality to a service provider and may be operated by the service provider or on behalf of the service provider. The processing circuitry 42 of the host computer 24 may enable the host computer 24 to observe, monitor, control, transmit to and/or receive from the network node 16 and or the wireless device 22.

The communication system 10 further includes a network node 16 provided in a communication system 10 and including hardware 58 enabling it to communicate with the host computer 24 and with the WD 22. The hardware 58 may include a communication interface 60 for setting up and maintaining a wired or wireless connection with an interface of a different communication device of the communication system 10, as well as a radio interface 62 for setting up and maintaining at least a wireless connection 64 with a WD 22 located in a coverage area 18 served by the network node 16. The radio interface 62 may be formed as or may include, for example, one or more RF transmitters, one or more RF receivers, and/or one or more RF transceivers. The communication interface 60 may be configured to facilitate a connection 66 to the host computer 24. The connection 66 may be direct or it may pass through a core network 14 of the communication system 10 and/or through one or more intermediate networks 30 outside the communication system 10.

In the embodiment shown, the hardware 58 of the network node 16 further includes processing circuitry 68. The processing circuitry 68 may include a processor 70 and a memory 72. In particular, in addition to or instead of a processor, such as a central processing unit, and memory, the processing circuitry 68 may comprise integrated circuitry for processing and/or control, e.g., one or more processors and/or processor cores and/or FPGAs (Field Programmable Gate Array) and/or ASICs (Application Specific Integrated Circuitry) adapted to execute instructions. The processor 70 may be configured to access (e.g., write to and/or read from) the memory 72, which may comprise any kind of volatile and/or nonvolatile memory, e.g., cache and/or buffer memory and/or RAM (Random Access Memory) and/or ROM (Read-Only Memory) and/or optical memory and/or EPROM (Erasable Programmable Read-Only Memory).

Thus, the network node 16 further has software 74 stored internally in, for example, memory 72, or stored in external memory (e.g., database, storage array, network storage device, etc.) accessible by the network node 16 via an external connection. The software 74 may be executable by the processing circuitry 68. The processing circuitry 68 may be configured to control any of the methods and/or processes described herein and/or to cause such methods, and/or processes to be performed, e.g., by network node 16. Processor 70 corresponds to one or more processors 70 for performing network node 16 functions described herein. The memory 72 is configured to store data, programmatic software code and/or other information described herein. In some embodiments, the software 74 may include instructions that, when executed by the processor 70 and/or processing circuitry 68, causes the processor 70 and/or processing circuitry 68 to perform the processes described herein with respect to network node 16. For example, processing circuitry 68 of the network node 16 may include a mapping unit 32 which may be configured to map each of a plurality of channel quality values, according to a first mapping function, the first mapping function being based at least in part on a base rank and a second rank. The network node may also include a statistics unit 34 which is configured to determine a base mean and a base variance of the mapped channel quality values. The mapping unit 32 may also be configured to map a second mean according to a second mapping function, the second mapping function being based on the base rank and a selected rank, to determine an estimated channel quality value expected at a future time for the selected rank. The statistics unit 34 may also be configured to, given a first mapped channel quality value of the mapped channel quality values, determine a second mean and a second variance based at least in part on the base mean, the base variance, the first mapped channel quality value and an autocorrelation of the mapped channel quality values.

The communication system 10 further includes the WD 22 already referred to. The WD 22 may have hardware 80 that may include a radio interface 82 configured to set up and maintain a wireless connection 64 with a network node 16 serving a coverage area 18 in which the WD 22 is currently located. The radio interface 82 may be formed as or may include, for example, one or more RF transmitters, one or more RF receivers, and/or one or more RF transceivers.

The hardware 80 of the WD 22 further includes processing circuitry 84. The processing circuitry 84 may include a processor 86 and memory 88. In particular, in addition to or instead of a processor, such as a central processing unit, and memory, the processing circuitry 84 may comprise integrated circuitry for processing and/or control, e.g., one or more processors and/or processor cores and/or FPGAs (Field Programmable Gate Array) and/or ASICs (Application Specific Integrated Circuitry) adapted to execute instructions. The processor 86 may be configured to access (e.g., write to and/or read from) memory 88, which may comprise any kind of volatile and/or nonvolatile memory, e.g., cache and/or buffer memory and/or RAM (Random Access Memory) and/or ROM (Read-Only Memory) and/or optical memory and/or EPROM (Erasable Programmable Read-Only Memory).

Thus, the WD 22 may further comprise software 90, which is stored in, for example, memory 88 at the WD 22, or stored in external memory (e.g., database, storage array, network storage device, etc.) accessible by the WD 22. The software 90 may be executable by the processing circuitry 84. The software 90 may include a client application 92. The client application 92 may be operable to provide a service to a human or non-human user via the WD 22, with the support of the host computer 24. In the host computer 24, an executing host application 50 may communicate with the executing client application 92 via the OTT connection 52 terminating at the WD 22 and the host computer 24. In providing the service to the user, the client application 92 may receive request data from the host application 50 and provide user data in response to the request data. The OTT connection 52 may transfer both the request data and the user data. The client application 92 may interact with the user to generate the user data that it provides.

The processing circuitry 84 may be configured to control any of the methods and/or processes described herein and/or to cause such methods, and/or processes to be performed, e.g., by WD 22. The processor 86 corresponds to one or more processors 86 for performing WD 22 functions described herein. The WD 22 includes memory 88 that is configured to store data, programmatic software code and/or other information described herein. In some embodiments, the software 90 and/or the client application 92 may include instructions that, when executed by the processor 86 and/or processing circuitry 84, causes the processor 86 and/or processing circuitry 84 to perform the processes described herein with respect to WD 22.

In some embodiments, the inner workings of the network node 16, WD 22, and host computer 24 may be as shown in FIG. 3 and independently, the surrounding network topology may be that of FIG. 2.

In FIG. 3, the OTT connection 52 has been drawn abstractly to illustrate the communication between the host computer 24 and the wireless device 22 via the network node 16, without explicit reference to any intermediary devices and the precise routing of messages via these devices. Network infrastructure may determine the routing, which it may be configured to hide from the WD 22 or from the service provider operating the host computer 24, or both. While the OTT connection 52 is active, the network infrastructure may further take decisions by which it dynamically changes the routing (e.g., on the basis of load balancing consideration or reconfiguration of the network). The wireless connection 64 between the WD 22 and the network node 16 is in accordance with the teachings of the embodiments described throughout this disclosure. One or more of the various embodiments improve the performance of OTT services provided to the WD 22 using the OTT connection 52, in which the wireless connection 64 may form the last segment. More precisely, the teachings of some of these embodiments may improve the data rate, latency, and/or power consumption and thereby provide benefits such as reduced user waiting time, relaxed restriction on file size, better responsiveness, extended battery lifetime, etc.

In some embodiments, a measurement procedure may be provided for the purpose of monitoring data rate, latency and other factors on which the one or more embodiments improve. There may further be an optional network functionality for reconfiguring the OTT connection 52 between the host computer 24 and WD 22, in response to variations in the measurement results. The measurement procedure and/or the network functionality for reconfiguring the OTT connection 52 may be implemented in the software 48 of the host computer 24 or in the software 90 of the WD 22, or both. In embodiments, sensors (not shown) may be deployed in or in association with communication devices through which the OTT connection 52 passes; the sensors may participate in the measurement procedure by supplying values of the monitored quantities exemplified above, or supplying values of other physical quantities from which software 48, 90 may compute or estimate the monitored quantities. The reconfiguring of the OTT connection 52 may include message format, retransmission settings, preferred routing etc.; the reconfiguring need not affect the network node 16, and it may be unknown or imperceptible to the network node 16. Some such procedures and functionalities may be known and practiced in the art. In certain embodiments, measurements may involve proprietary WD signaling facilitating the host computer’s 24 measurements of throughput, propagation times, latency and the like. In some embodiments, the measurements may be implemented in that the software 48, 90 causes messages to be transmitted, in particular empty or ‘dummy’ messages, using the OTT connection 52 while it monitors propagation times, errors, etc.

Thus, in some embodiments, the host computer 24 includes processing circuitry 42 configured to provide user data and a communication interface 40 that is configured to forward the user data to a cellular network for transmission to the WD 22. In some embodiments, the cellular network also includes the network node 16 with a radio interface 62. In some embodiments, the network node 16 is configured to, and/or the network node’s 16 processing circuitry 68 is configured to perform the functions and/or methods described herein for preparing/initiating/maintaining/ supporting/ending a transmission to the WD 22, and/or preparing/terminating/ maintaining/supporting/ending in receipt of a transmission from the WD 22.

In some embodiments, the host computer 24 includes processing circuitry 42 and a communication interface 40 that is configured to a communication interface 40 configured to receive user data originating from a transmission from a WD 22 to a network node 16. In some embodiments, the WD 22 is configured to, and/or comprises a radio interface 82 and/or processing circuitry 84 configured to perform the functions and/or methods described herein for preparing/initiating/maintaining/ supporting/ending a transmission to the network node 16, and/or preparing/ terminating/maintaining/supporting/ending in receipt of a transmission from the network node 16.

Although FIGS. 2 and 3 show various “units” such as mapping unit 32 and statistics unit 34 as being within a respective processor, it is contemplated that these units may be implemented such that a portion of the unit is stored in a corresponding memory within the processing circuitry. In other words, the units may be implemented in hardware or in a combination of hardware and software within the processing circuitry.

FIG. 4 is a flowchart illustrating an exemplary method implemented in a communication system, such as, for example, the communication system of FIGS. 2 and 3, in accordance with one embodiment. The communication system may include a host computer 24, a network node 16 and a WD 22, which may be those described with reference to FIG. 3. In a first step of the method, the host computer 24 provides user data (Block SI 00). In an optional substep of the first step, the host computer 24 provides the user data by executing a host application, such as, for example, the host application 50 (Block SI 02). In a second step, the host computer 24 initiates a transmission carrying the user data to the WD 22 (Block SI 04). In an optional third step, the network node 16 transmits to the WD 22 the user data which was carried in the transmission that the host computer 24 initiated, in accordance with the teachings of the embodiments described throughout this disclosure (Block SI 06). In an optional fourth step, the WD 22 executes a client application, such as, for example, the client application 92, associated with the host application 50 executed by the host computer 24 (Block SI 08).

FIG. 5 is a flowchart illustrating an exemplary method implemented in a communication system, such as, for example, the communication system of FIG. 2, in accordance with one embodiment. The communication system may include a host computer 24, a network node 16 and a WD 22, which may be those described with reference to FIGS. 2 and 3. In a first step of the method, the host computer 24 provides user data (Block SI 10). In an optional substep (not shown) the host computer 24 provides the user data by executing a host application, such as, for example, the host application 50. In a second step, the host computer 24 initiates a transmission carrying the user data to the WD 22 (Block SI 12). The transmission may pass via the network node 16, in accordance with the teachings of the embodiments described throughout this disclosure. In an optional third step, the WD 22 receives the user data carried in the transmission (Block SI 14).

FIG. 6 is a flowchart illustrating an exemplary method implemented in a communication system, such as, for example, the communication system of FIG. 2, in accordance with one embodiment. The communication system may include a host computer 24, a network node 16 and a WD 22, which may be those described with reference to FIGS. 2 and 3. In an optional first step of the method, the WD 22 receives input data provided by the host computer 24 (Block SI 16). In an optional substep of the first step, the WD 22 executes the client application 92, which provides the user data in reaction to the received input data provided by the host computer 24 (Block SI 18). Additionally or alternatively, in an optional second step, the WD 22 provides user data (Block S120). In an optional substep of the second step, the WD provides the user data by executing a client application, such as, for example, client application 92 (Block S122). In providing the user data, the executed client application 92 may further consider user input received from the user. Regardless of the specific manner in which the user data was provided, the WD 22 may initiate, in an optional third substep, transmission of the user data to the host computer 24 (Block S124). In a fourth step of the method, the host computer 24 receives the user data transmitted from the WD 22, in accordance with the teachings of the embodiments described throughout this disclosure (Block S126).

FIG. 7 is a flowchart illustrating an exemplary method implemented in a communication system, such as, for example, the communication system of FIG. 2, in accordance with one embodiment. The communication system may include a host computer 24, a network node 16 and a WD 22, which may be those described with reference to FIGS. 1 and 2. In an optional first step of the method, in accordance with the teachings of the embodiments described throughout this disclosure, the network node 16 receives user data from the WD 22 (Block S128). In an optional second step, the network node 16 initiates transmission of the received user data to the host computer 24 (Block SI 30). In a third step, the host computer 24 receives the user data carried in the transmission initiated by the network node 16 (Block SI 32).

FIG. 8 is a flowchart of an exemplary process in a network node 16 for estimating an expected downlink signal to interference ratio and associated uncertainty for use in link adaptation. One or more blocks described herein may be performed by one or more elements of network node 16 such as by one or more of processing circuitry 68 (including the mapping unit 32 and statistics unit 34), processor 70, radio interface 62 and/or communication interface 60. Network node 16 such as via processing circuitry 68 and/or processor 70 and/or radio interface 62 and/or communication interface 60 is configured to map each of a plurality of channel quality values according to a first mapping function, the first mapping function being based at least in part on a base rank and a second rank (Block SI 34). The method also includes determining a base mean and a base variance of the mapped channel quality values (Block S136). The method further includes, given a first mapped channel quality value of the mapped channel quality values, determining a second mean and a second variance based at least in part on the base mean, the base variance, the first mapped channel quality value and an autocorrelation of the mapped channel quality values (Block S138). The method also includes mapping the second mean according to a second mapping function, the second mapping function being based at least in part on the base rank and a selected rank, to determine an estimated channel quality value expected at a future time for the selected rank (Block S140). According to this aspect, in some embodiments, the method includes comparing the second variance to a first threshold, the first threshold being associated with a maximum block error probability, BLEP. In some embodiments, the method also includes comparing the second variance to a second threshold, the second threshold being associated with a minimum block error probability, BLEP. In some embodiments, the second mapping function is based at least in part on an inverse of the first mapping function. In some embodiments, the first mapping function includes: where Q is a measured signal channel quality value, RIbase is a base rank indicator and RI is a reported rank indicator, kiu is a constant selected based at least in part on whether the base rank is greater than the second rank. In some embodiments, the method further includes measuring the plurality of channel quality values at successive times based at least in part on a rank reported by the WD and a channel quality index, CQI, reported by the WD. In some embodiments, the plurality of measured channel quality values are filtered prior to determining the base mean and the base variance. In some embodiments, the plurality of measured channel quality values are weighted to give greater weight to more recent measured channel quality values than weight given to less recent measured channel quality values. In some embodiments, determining the second mean and the second variance further includes interpolating the autocorrelation of the mapped channel quality values. In some embodiments, the method also includes determining the plurality of channel quality values based at least in part on a random variable. In some embodiments, determining the base mean and the base variance is based at least in part on determining a mean and variance for each of a plurality of sub-bands. In some embodiments, the second rank is one of a rank intended for downlink transmission and a rank last reported by the WD, and wherein the selected rank is the rank intended for downlink transmission. In some embodiments, a channel quality value is a signal to interference plus noise ratio, SINR.

Having described the general process flow of arrangements of the disclosure and having provided examples of hardware and software arrangements for implementing the processes and functions of the disclosure, the sections below provide details and examples of arrangements for estimating an expected downlink signal to interference ratio or other expected channel quality and associated uncertainty for use in link adaptation.

The following examples use SINR as the channel quality value to be measured or predicted. However, other measures of channel quality, such as received signal power, may be employed in the following example embodiments in place of SINR. Embodiment 0: Using expected SINR and SINR uncertainty in link adaptation

In some embodiments, the expected SINR and SINR uncertainty is represented as the mean μ and std σ (or variance σ ² ) The expected SINR is the SINR that is predicted by calculations involving measurements of SINR. “Measurements of SINR” or “SINR measurements” refer to SINR determined based at least in part on CSI reported by the WD 22. When the term standard deviation (std) is used, it is understood that the standard deviation can be obtained from the square root of the variance which may be computed based at least in part on measurements of SINR. Conversely, the variance can be obtained from the square of the standard deviation which may be computed based at least in part on measurements of SINR. For example, SINR uncertainty may be expressed as a standard deviation or as a variance.

In a first example, the statistics unit 34 of the processing circuitry 68 of the network node 16 may select an MCS yielding a transport block size TBS _MCS such that the product is maximized, where is a function of MCS, the mean μ and std σ. The may be written as the value of an integral: where is a selected probability density function having mean p and std σ and is a function yielding the BLEP at a SINR = x. In some embodiments, may be a normal probability density function with mean p and std σ. In some embodiments, may be a skew probability density function, e.g., a skew normal distribution with skewness y.

In a second example, the statistics unit 34 may select a MCS that maximizes transport block size TBS _MCS so that the BLEP at SINR = p is at most BLEP (σ), where BLEP(σ) is a function of the std σ. This means that BLEP (σ) serves as a target BLEP for link adaptation while μ will be the SINR _{used-prediction} . Preferably, BLEP(σ) is a non-decreasing function with a. For example, the function BLEP(σ) may be defined as: where BLEP _max > BLEP _min and σ _max min are selected design parameters.

For example, if the true std is small then it may be difficult to produce an accurate estimate for the uncertainty which means that σ may be inaccurate. Consequently, it may be difficult to know what a corresponding suitable target BLEP should be. When re-transmissions are possible, a target BLEP applied in link adaption that is too small may result in a less spectrally-efficient MCS being selected. Therefore, it may be preferable to select a minimum std, and minimum BLEP _min . However, if no re-transmissions are possible, e.g., due to latency constraints, it may be preferable to set a minimum std, σ _mln , because the estimated σ will be very close to zero and inaccurate due to CSI quantization effects. In some cases, the true std is considerably larger. BLEP _max and a _max are upper limits on BLEP and std. In principle, any BLEP _max < 1 may work, but it may be preferable to select a MCS that does not result in incorrect decoding with very high probability. In some embodiments, std is replaced with variance, and these terms are used interchangeably except where otherwise noted. In some embodiments, σ may be replaced with σ ² or vice versa, and the thresholds σ _mln and a _max may be interchanged with their corresponding variance measures ^and Embodiment 1 : Determining expected SINR and SINR uncertainty from CSI from a single CSI process

In some embodiments, CSI is reported by the WD 22 where the CSI is from a single CSI process. Without rank or pre-coder restrictions, each reported CSI may include a three-tuple {RI, PMI, CQI} where RI is the rank indicator, PMI is the precoder matrix indicator and CQI is the channel quality indicator. For a given CQI, a SINR for corresponding rank may be determined, e.g., by a function f _RI (CQI) that converts RI and CQI to a corresponding SINR measurement SINR _{RI CQI} . In some embodiments, the network node 16 such as by mapping unit 32 may select a rank to be used as a base rank RI _base for SINR values, and all SINR measurements are mapped to RI _base - This may be achieved by selecting a function ) In some embodiments, the function m ^{ay be} selected ^as: where SINR is in linear scale and RI = 0, 1, ... represents rank 1, 2, ... By mapping the SINR values SINR _{RI CQI} to base SINR values in a common unit given by the base rank, the statistics unit 34 can determine statistical measures such as mean and std in the unit represented by the base rank. For example, from a sequence of base SINR measurements, a base mean g _base = c ^an be determined. In another example, a scaling factor k _RI may be applied by the mapping unit 32 as follows: where the scale factor may compensate for layer non-orthogonality and/or reduced or increased interference suppression when mapping to a different rank. For example, k _RI > 1 may be selected when RI is higher than the RI _base and k _RI < 1 may be selected when RI is higher than RI _base . The function may be more generally shown a where Q is a measured channel quality value.

To each reported CSIi there is a time that is associated with the reported CSI. In some cases, there are multiple time instances that are associated with reported CSI. For example, the channel may be measured in a first time instance while interference and noise is measured in a second time instance. In such cases, the network node 16 may determine a single time instance to be associated with the reported CSI, e.g., by taking the mean value The single time instance may also be determined as a median value, or a minimum or maximum of the multiple time instances, for example. From the CSI reports, CSI _i , and time instances, t _i , the statistics unit 34 may determine mean and std in units of the base rank and may also determine timecorrelation in the same units. For example, if the CSI reports arrive periodically, the base auto-correlation of the base SINR could be determined at discrete time differences. From the base mean p _base and base std σ _base , ^a probability density function Pμ _base , σ _base (*) for the base SINR may be determined. By assuming a base SINR being normally distributed, a conditional probability density function given the last observed base SINR can be determined.

More precisely, it is known from probability theory that if X and Y are correlated normal random variables each with mean μ and std σ, the probability density function given Y = y is normally distributed having mean . , , . is the correlation between X and Y. Note that if X and Y belong to the same statistical process X(t), then X and Y would be realizations at two time instances, say The covariance between X and Y would then be the auto-correlation Since CSI reports are not transmitted all the time, d(r) is only known at discrete values for T. However, an interpolation may be performed to provide approximate values of d(r) in between the discrete values determined from the CSI reports..

In some examples, CSI may be requested aperiodic-ally, which makes it more complicated to determine A(T) . In some embodiments, CSI may be determined over a period that equals the average time between two consecutive reports. In some embodiments, CSI may be assumed to remain the same between reports and that same value may be artificially injected at a new time. In some embodiments, a structure in the auto-correlation function is assumed such that each independent auto-correlation value is filtered into a continuous function. In some embodiments, if CSI _i and CSI ₂ are received at times t ₁ and t ₂ > t ₁ , respectively, yielding base SINR _{base l} and SINR _{base 2} , the network node 16 may artificially inject interpolated values between t ₁ and t ₂ . Suppose Δ is the smallest value for which T is needed (or smallest time between two consecutive CSI reports). The network node 16, via the statistics unit 34, may inject interpolated values, where interpolated values may be determined as:

Suppose the last base SINR equals y. Then a conditional base mean and base std representing the expected base SINR and base uncertainty at a future time instance can be determined. When an expected SINR and uncertainty for a certain RI that is not the base RI is to be determined, a (conditional) mean μ and std σ may be determined. The conditional mean μ may be determined using a function that is the inverse of while σ may be determined to be the same as the conditional base std. In some examples, σ may be determined by scaling the conditional base std, where the scaling factor may be a fixed value or a value that depends on the current RI and the RI _base - For example, if RI < RI _base , ^a scale factor lower than one may be used for better interference suppression when a lower rank is used.

In the examples above, older CSI reports may be excluded, weighted or filtered before determining estimates of mean and variance (or std). It may be advantageous to exclude, weight or filter the CSI reports because of changing channel and interference conditions. The expected SINR and uncertainty to be estimated should be the values that prevail at the time of transmission. Therefore, in some embodiments, a time window from a selected previous time to the current time may be applied. This can be achieved by various filtering methods. For example, the mean could be a filtered mean using the filter where y _n is the filtered value at the time indexed by n, and x _n is the value of the estimate at the time indexed by n and where a is a filtering coefficient.

Embodiment 2: Determining expected SINR and SINR uncertainty from CSI from multiple CSI process

In some embodiments, the WD 22 may be configured with multiple CSI processes, e.g., one for each rank. Then CSI and consequently, SINR measurements, may be obtained for each rank frequently enough to calculate statistics per rank. Thus, a rank-specific mean, std and auto-correlation may be determined which enables determination of conditional mean and std representing the expected SINR and uncertainty for each rank. In some examples, fewer CSI processes than the number of different ranks may be configured. For example, if the maximum rank is 4, then a first CSI process may be configured for reported rank of 1 or 2, while second CSI process may configured for reported rank of 3 or 4. Then, the methods in Embodiment 1 may be applied twice in parallel. The method of Embodiment 1 may be applied for reports associated with a first CSI process and the method of Embodiment 1 may be applied for reports associated with a second CSI process. These applications of the method of Embodiment 1 may be performed at the same time. In such examples, if the last CSI report indicated rank is 3 or 4, then link adaption could use the conditional mean and std from the second CSI process. Otherwise, the conditional mean and std from the first CSI process may be used.

Embodiment 3 : Determining expected SINR and SINR uncertainty for reciprocitybased beamforming

In some embodiments, an SINR from a latest CSI report is determined as explained above and is referred to as SINR _{RI reciproc} . The SINR corresponding to a reported CQI is referred to as SINR _RI . Based on CSI reports this embodiment also may use the methods in Embodiment 1 and/or Embodiment 2 to determine a conditional mean μ and std σ for SINR with respect to the last reported rank (RI). Note that μ and σ may represent the expected SINR and uncertainty with the assumption that the transmission occurs with a pre-coder that is codebook-based. When the pre-coder is not codebook-based and determined from SRS, the network node 16 may estimate an expected SINR and uncertainty, μ _reciproc and σ _reciproc, given a non-codebook-based pre-coder.

One way to determine μ _reciproc and σ _reciproc is to perform the methods of Embodiments 1 and 2 with realizations of SINR _CSI taken from a random variable with a distribution having mean R and std σ, and then take a sample mean and std from the realizations as an estimate for μ _reciproc and σ _reciproc

Another way to determine μ _reciproc and σ _reciproc is based on the observation that the covariance matrix formulas may involve only the strength of interference. This means that the difference SINR _{RI reciproc} — SINR _RI is constant given the same channel, reported pre-coder and determined pre-coder, i.e., if only the strength of interference changes. Further, this means that the std of SINR _{RI reciproc} will be the same as the variance of SINR _CSI if variations are due to interference strength changes only. This observation enables the network node 16 to directly produce an estimate for μ _reciproc and σ _reciproc as follows.

A third way to determine μ _reciproc and (σ _reciproc is by applying the methods in Embodiment 1 and/or Embodiment 2 based on SINR _{RI reciproc} , rather than SINR _RI deduced from CSI reports. In some embodiments, SINR _{RI reciproc} may be used as input to the methods in Embodiment 1 and/or Embodiment 2 to determine estimates of μ _reciproc and ( σ _reciproc based on the latest channel measurement, but using a previously-determined pre-coder. A reason for selecting the previously determined pre-coder instead of the currently determined pre-coder may be to capture pre-coder uncertainty. If MU-MIMO scheduling is used, then the transmit power may be shared among the co-scheduled WDs. In such cases, the above methods may still apply with the assumption that SINR _{RI reciproc} is determined with respect to the total transmit power divided among the co-scheduled WDs. Note that the estimate of variance or standard deviation can be used even if reciprocity beamforming is not used, or even if measurements are performed on a different frequency if similar statistical properties of the channel can be assumed. Note that this estimate may only cover variations in the channel, including precoder mismatch, but not interference. Hence a correction term for (σ _reciproc may be added. This correction term may be computed based on, for example, CSI-reports and/or path gain measurements and scheduling information from surrounding cells.

Embodiment 4: Sub-band CSI reports

In some embodiments, sub-band CQI is reported by the WD 22, i.e., each CSI report comprises CQI for N sub-bands. In some embodiments, the network node 16 applies the methods in Embodiment 1 and/or Embodiment 2 for each sub-band. This means that link adaption is given N two-tuples of conditional mean and std, i.e. To perform link adaption as in Embodiment 0, link adaptation may include estimating, via the statistics unit 34, a conditional mean and std prevailing on the sub-bands used for the transmission. If the transmission covers the sub-bands in the set S, then conditional mean and std may be estimated based on the {/q, σJ, i E S. In some embodiments, it is assumed that in the dB- domain, the SINR over a set of sub-bands is equal to the mean value of SINR over the set of sub-bands, i.e. by assuming: where /q are assumed to be in the dB-domain and |S| is the cardinality of S. Determining the conditional std (or variance) is complicated by the fact that the subbands are typically correlated. Therefore, in some embodiments, the methods in Embodiment 1 and/or Embodiment 2 may be extended to also perform estimation of covariance C _ij between sub-band i and j. Note that c _{i i} = σ ² as is known from probability theory, given the assumptions above. Then the conditional std σ for the transmission sub-bands may be determined from:

The complexity of determining the conditional std may be reduced by various assumptions. For example, it may be assumed that the sub-bands are un-correlated which may lead to an overestimate of σ . Such assumptions may be suitable when it is important that the estimate be not under-estimated. For example, if the link adaptation is performed for a high-reliable transmission it may be more important that reliability is fulfilled than that the transmission is performed as spectrally-efficient as possible. In such cases, it may be suitable to over-estimate σ to have margins to various errors such as CQI quantization errors and/or estimation inaccuracies arising from the fact that neither μ _i nor can be assumed to be fully stationary in time. Several other simplifications are possible. For example, σ or σ ² could be determined as the mean, median, minimum or maximum over or σ ² .

Embodiment 5: Determination of expected SINR and SINR uncertainty for rank override

In some embodiments, an expected SINR and SINR uncertainty may be desired for a transmission using a rank different than the last reported rank. This is typically what a link adaption according to Embodiment 0 may need to determine when the transmission is a physical downlink control channel (PDCCH) transmission. This is so because the CSI is reported with respect to the physical downlink shared channel (PDSCH), not the PDCCH. The methods in Embodiment 1 and/or Embodiment 2 may also be applied in some embodiments where the conditional mean and std are determined for the intended rank and not the last received rank.

As will be appreciated by one of skill in the art, the concepts described herein may be embodied as a method, data processing system, computer program product and/or computer storage media storing an executable computer program. Accordingly, the concepts described herein may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects all generally referred to herein as a “circuit” or “module.” Any process, step, action and/or functionality described herein may be performed by, and/or associated to, a corresponding module, which may be implemented in software and/or firmware and/or hardware. Furthermore, the disclosure may take the form of a computer program product on a tangible computer usable storage medium having computer program code embodied in the medium that can be executed by a computer. Any suitable tangible computer readable medium may be utilized including hard disks, CD-ROMs, electronic storage devices, optical storage devices, or magnetic storage devices.

Some embodiments are described herein with reference to flowchart illustrations and/or block diagrams of methods, systems and computer program products. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer (to thereby create a special purpose computer), special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer readable memory or storage medium that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instruction means which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.

It is to be understood that the functions/acts noted in the blocks may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Although some of the diagrams include arrows on communication paths to show a primary direction of communication, it is to be understood that communication may occur in the opposite direction to the depicted arrows.

Computer program code for carrying out operations of the concepts described herein may be written in an object oriented programming language such as Python, Java® or C++. However, the computer program code for carrying out operations of the disclosure may also be written in conventional procedural programming languages, such as the "C" programming language. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer. In the latter scenario, the remote computer may be connected to the user's computer through a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).

Many different embodiments have been disclosed herein, in connection with the above description and the drawings. It will be understood that it would be unduly repetitious and obfuscating to literally describe and illustrate every combination and subcombination of these embodiments. Accordingly, all embodiments can be combined in any way and/or combination, and the present specification, including the drawings, shall be construed to constitute a complete written description of all combinations and subcombinations of the embodiments described herein, and of the manner and process of making and using them, and shall support claims to any such combination or subcombination.

It will be appreciated by persons skilled in the art that the embodiments described herein are not limited to what has been particularly shown and described herein above. In addition, unless mention was made above to the contrary, it should be noted that all of the accompanying drawings are not to scale. A variety of modifications and variations are possible in light of the above teachings without departing from the scope of the following claims.